Governments, businesses and even individuals are seeking more effective and efficient methods for improving traffic control and increasing the security at vehicle entry points to physical locations, particularly for secure facilities. Various technology solutions can identify a given vehicle at an entry point, and searches can be undertaken, both externally and internally, to identify any potential threats. To a limited degree, some technology solutions can identify drivers and passengers in a vehicle at an entry point, but such solutions require the occupant(s) such as the driver and/or passenger to stop, open the window and present some form of identification document, such as a photo identification or RFID proximity card, for example, or some form of biometric information that may be scanned by facial or retinal cameras, for example. This vehicle occupant identification process is time consuming and often not practical to handle high traffic volume. Further, the extra identification time may also not be appropriate for vehicles carrying special privilege occupants that are not willing to undergo routine security procedures.
In addition, efforts to inspect vehicle interiors through a barrier such as a window, or while a vehicle is moving, face constraints. For example, significant variability exists in ambient and vehicle cabin lighting conditions, weather conditions, window reflectivity, and window tint. These variations raise numerous challenges to conventional imagery-based identification systems. For example, light reflection from a window surface can render an image nearly useless, and heavy glass tinting can make identifying an individual inside a vehicle next to impossible. Another challenge with face detection in vehicle interiors through glass and other barriers is the fact that the human occupants in the vehicles can be occluded. For example, face occlusion problems can be caused by opaque parts of the vehicle blocking the occupant's face, the motion of occupants and the accidental positioning of hands on the face. Further, face detection algorithms do not perform well on face profile images. Face detection algorithms are designed for frontal face profiles.
Solutions are needed that allow for a rapid and minimally invasive facial detection and identification of vehicle occupants and contents. Further, solutions are needed that overcome the challenges associated with variable lighting, weather conditions, window tint, and light reflection. When combined with other forms of identification, such as the vehicle's license plate, under-vehicle scanned imagery, radio-frequency identification tags, facial detection provides an additional level of authentication that can provide both enhanced security with the ability to identify vehicles and their occupants. Additionally, by clearly identifying occupants in a given vehicle as part of a multi-factor authentication process, the throughput, or rate at which vehicles can pass through a security checkpoint, can be significantly enhanced.